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This paper proposes a new approach of band selection for classifying multiple objects in hyperspectral images. Different from traditional algorithms, we construct a semisupervised pair-wise band selection (PWBS) framework for this task, in which an individual band selection process is performed only for each pair of classes. First, the statistical parameters for spectral features of each class, including...
This paper proposes a new framework of band selection for object classification in hyperspectral images. Different from traditional approaches where the selected bands are shared from all classes, in this work, different subsets of bands are selected for different class pairs. Without prior knowledge of spectral database, we estimate the spectral characteristic of objects with the labeled and unlabeled...
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